Motor Bearing Fault Diagnosis Based on Fourier-based Synchrosqueezing Transform and Hilbert Envelope Spectrum

Motor’s current signal contains information about its operation status and thus can be used to diagnose motor bearing faults. To solve the problem that the stator current changes are generally subtle and the traditional signal processing methods cannot accurately extract the signal fault characteristic frequency, this paper proposes a stator-current-based motor bearing fault diagnosis method based on Fourier-based Synchrosqueezing Transform (FSST) and Hilbert envelope spectrum. The method uses FSST to decompose the signal into a set of Intrinsic Mode Function (IMF) components containing bearing fault characteristic information, selects the appropriate IMF components for current signal reconstruction, and then combines the Hilbert envelope spectrum with the spectrum analysis of the reconstructed signals to obtain a clear fault characteristic frequency. Compared with the traditional method, the proposed method effectively suppresses modal mixing and noise interference, improving the effectiveness and accuracy of fault diagnosis.